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INDONESIA
Jurnal Statistika dan Aplikasinya
Published by Universitas Negeri Jakarta
ISSN : -     EISSN : 26208369     DOI : https://doi.org/10.21009/JSA.041
Core Subject : Economy, Science, Education, Agriculture, Social,
Agriculture, Biological Sciences & Forestry Computer Science & IT Economics, Econometrics & Finance Social Sciences Other
Jurnal Statistika dan Aplikasinya JSA is dedicated to all statisticians who wants to publishing their articles about statistics and its application. The coverage of JSA includes every subject that using or related to statistics.
Arjuna Subject : Matematika - Statistik dan Probabilitas
Articles 169 Documents
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Evaluasi Perbandingan Kinerja Algoritma Cheng and Church Biclustering Terhadap Algoritma Clustering Klasik K-Means untuk Mengidentifikasi Pola Distribusi Barang Ekspor Indonesia Baehera, Seta; Utami Dyah Syafitri; Agus Mohamad Soleh
Jurnal Statistika dan Aplikasinya Vol. 7 No. 2 (2023): Jurnal Statistika dan Aplikasinya
Publisher : LPPM Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.07204

Abstract

Clustering is a process of grouping data into several groups (clusters) so that data in one cluster has a homogeneous level of similarity and data between clusters has heterogeneous similarity. A common example of a clustering algorithm is K-Means Clustering. Compared with classical clustering algorithms, the biclustering algorithm is a two-dimensional data grouping process. The biclustering algorithm functions to find data submatrices, namely row subgroups and column subgroups that have high correlation. One example of a biclustering algorithm is Cheng and Church Biclustering (CC Biclustering). The aim of this research is to evaluate the performance of the biclustering algorithm against classical clustering algorithms. Analysis applied to CC Biclustering and K-Means Clustering to identify distribution patterns of Indonesian export goods in the period 2013 to 2022. Based on research results, the optimal scenario for the K-Means algorithm is scenario 2, that is the application of the 7 cluster K-Means algorithm with pre- processing data scaling. Meanwhile, the optimal scenario for the CC Biclustering algorithm is scenario 1, that is the application of the CC Biclustering algorithm with a tolerance value of 0.10 with data scaling pre-processing. However, from these two scenarios, based on the MSR/Volume value, it was concluded that the best scenario is scenario 1 in the application of the CC Biclustering algorithm which has an MSR/Volume value of 0.077.
Pemodelan Besar Klaim menggunakan Distribusi Berekor dan Tail-Value-at-Risk (TVaR) pada Data Sampel Badan Penyelenggara Jaminan Sosial (BPJS) Kesehatan Widodo, Vicko Regenio; Adiyansyah, Firman; Anwar, Yusril Rais; Sari, Kurnia Novita
Jurnal Statistika dan Aplikasinya Vol. 7 No. 2 (2023): Jurnal Statistika dan Aplikasinya
Publisher : LPPM Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.07203

Abstract

Information about amount of insurance claims is needed by insurer to set premium or other decisions in the future. Amount of claims modeling is a way to determine the characteristics of a distribution of claims data and can be used to predict the amount of claims that may occur. A commonly used model for amount of insurance claims data is the distribution model for heavy tails. The discussion in this article focuses on modeling amount of insurance claims using Lognormal, Pareto and Weibull distributions, and also using Gamma distribution for comparison. The data used is sample data from Badan Penyelenggara Jaminan Sosial (BPJS) Kesehatan in 2015-2016. The data contains membership data and details of the amount of each participant's claim. The analysis is carried out to find out the best candidate model that matches the amount of insurance claims data for both inpatient and outpatient categories. In addition, the Tail-Value-at-Risk (TVaR) of the model will be calculated to determine the amount of capital that will be required with a 75% confidence level. Based on the results of the study, the best model for large data samples of claims for outpatient and inpatient categories is the Lognormal model. TVaR for the outpatient category is Rp492,596 and for the inpatient category is Rp7,672,726.
Ambang Batas Reasuransi Non-Proporsional Menggunakan Tail Value-At-Risk (TVaR) dari Distribusi Peluang Campuran Eurico, David; Afifatul Ayu Astiani; I Kadek Darma Arnawa; Bagas Caesar Suherlan; Utriweni
Jurnal Statistika dan Aplikasinya Vol. 7 No. 2 (2023): Jurnal Statistika dan Aplikasinya
Publisher : LPPM Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.07202

Abstract

One of the tasks of banking institutions is to channel funds to the public through loan products. Banking institutions transfer the risk of non-performing loans to insurance companies and then partially reinsured to reinsurers. The purpose of this study is to determine the non-proportional reinsurance threshold based on the risk of loss of the 20% largest loan principal, using the Tail Value-at-Risk (TVaR) method. The threshold value will be estimated using a sample of 5,000 loans principal. The loan characteristics can be described by a Mixture Gamma Distribution consisting of components with different weights and parameters. The weights and parameters are 19% from the Gamma distribution with parameters α = 2.45 and β = 0.04, 34.5% from Gamma with parameters α = 8.29 and β = 0.07, and 46.5% from Gamma with parameters α = 30 and β = 0.13. Analysis using TVaR produces a threshold value of 274.9 million rupiah. In real cases, if the claim value exceeds 274.9 million rupiah. The insurance company will bear a value of 274.9 million rupiah and the reinsurer will bear the difference in the size of the claim against the threshold limit.
Regresi Logistik Ordinal untuk Memodelkan Predikat Lulusan Perguruan Tingggi Fathurahman, M.; Orvanita; Darnah
Jurnal Statistika dan Aplikasinya Vol. 7 No. 2 (2023): Jurnal Statistika dan Aplikasinya
Publisher : LPPM Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.07201

Abstract

Logistic regression is an alternative model that can model the relationship between a categorical response variable and one or more categorical, continuous predictor variables, or a combination of categorical and continuous predictor variables. Based on the number of categories in the response variable, the logistic regression model consists of a dichotomous logistic regression model and a polychotomous regression model. The dichotomous logistic regression model is a logistic regression model that has two categories in the response variable and has a Bernoulli distribution. In comparison, the polychotomous logistic regression model is a logistic regression model that has three or more categories and a multinomial distribution. The polychotomous logistic regression model is divided into two models, namely the multinomial logistic regression model and ordinal logistic regression. This research aims to examine ordinal logistic regression modeling and its application to the predicate of graduates of the undergraduate program at the Faculty of Mathematics and Natural Sciences, Mulawarman University (FMIPA UNMUL) for the 2020 graduation period. The results of the research show that the factors that have a significant influence on the predicate of graduates of the FMIPA UNMUL undergraduate program are gender and admission route. Female graduates of the FMIPA UNMUL undergraduate program have a greater chance of achieving satisfactory and very satisfactory predicates compared to achieving a cum laude predicate. Graduates of the FMIPA UNMUL undergraduate program who are accepted through the SMMPTN admission route have a lower chance of achieving satisfactory and very satisfactory predicates compared to achieving a cum laude predicate.
Log Linier Binomial Negatif dalam Memodelkan Data Siswa Putus Sekolah Tingkat SMA/SMK Santi, Vera Maya; Fanya Izmi Hawa; Bagus Sumargo
Jurnal Statistika dan Aplikasinya Vol. 7 No. 2 (2023): Jurnal Statistika dan Aplikasinya
Publisher : LPPM Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.07209

Abstract

Log linear negative binomial model is an extension of the linear regression model that can be used to analyze count data in the form of a contingency table when dealing with overdispersion where the variance value is greater than the mean value. Research in the field of education often produces contingency table data, one of which is data on students dropping out of school. The 2018 BPS survey showed that the number of students dropping out of school at the secondary school level in West Java province had the highest number of dropouts. Unfortunately, quantitative research on dropout students is still rarely done. Log linear negative binomial model is applied to determine the factors that influence the number of SMA/SMK dropout students in West Java province in 2021. The results show that gender, type of middle school, school status, interaction of gender and school status, and the interaction of middle school type and school status significantly affects the number of students dropping out of school. Furthermore, SMA/SMK dropouts in the province of West Java are dominated by male students, vocational students, and public-school students. Females are 1% more risky to drop out than males in public schools, while males are 1% more risky to drop out than females in private schools. SMA students are 59% more risky to drop out than SMK students in public schools, while SMK students are 71% more risky to drop out than SMA students in private school.
Application of Geographically Weighted Regression for Modeling the Poverty Cases in Kalimantan, Indonesia Noerul Hanin; Irvan Meilandra; Naomi Nessyana Debataraja; Retno Pertiwi
Jurnal Statistika dan Aplikasinya Vol. 8 No. 1 (2024): Jurnal Statistika dan Aplikasinya
Publisher : LPPM Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.08101

Abstract

Poverty is one of the most global issues that remains a concern worldwide, including in Indonesia. Indonesia is among the top 100 poorest countries in the world, ranked 73rd, to be exact. Government wants to decrease the national poverty rate, as outlined in the 2021-2024 National Medium-Term Development Plan, with the expected percentage of poor people in Indonesia on 2024 being 6.5 to 7 percent. Unfortunately, the hope for a reduction in the poverty rate has not been achieved in several regions, such as in 4 out of 5 provinces in Kalimantan. Therefore, the analyzing factors causing poverty in the Kalimantan region is conducted using the Geographically Weighted Regression method in order to give clearly information for government to decrease the poor rate in this region. GWR (Geographically Weighted Regression) is an extension of the regression method. The equation parameters for each observation location differ from one location to another. The weighting function used were fixed gaussian, fixed bisquare, fixed tricube, adaptive gaussian, adaptive bisquare, and adaptive tricube. Based on R2 and AIC value, the best model is the model with fixed tricube function. The R2 score is 0.8952, while the AIC score is 155.83. The GWR model is better than OLS or global regression model. Thus, spatial analysis to see the factors affecting the percentage of poor people in each regency and city in Kalimantan, Indonesia has been successfully carried out.
Analisis Faktor Konfirmatori untuk Mengidentifikasi Peubah Indikator Utama dalam Pengukuran Peubah Laten Dian Handayani; Fakhirah Maryam; Faroh Ladayya; Irsyad Hasari
Jurnal Statistika dan Aplikasinya Vol. 7 No. 2 (2023): Jurnal Statistika dan Aplikasinya
Publisher : LPPM Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.07212

Abstract

Research on attitudes, preferences and behavior are research that involves latent variables. Latent variables are measured by observing some indicator variables. Indicator variables that measure a latent variable are selected based on the researcher's perspective, however, it is necessary to consider the previous of related research. Indicator variables need to have theoretical meaning, be valid and reliable in measuring latent variables. Confirmatory factor analysis (CFA) is a statistical method that can be used to determine the validity of the indicator variables. In this research, CFA is used to determine the main indicator variables that characterize the reasons for choosing a study program at a university by high school graduates (or equivalent). There are 20 indicator variables chosen to represent several latent factors such as image, job prospects, interests and campus facilities. The results indicate that the latent factors of image, job prospects, interest and facilities can be respectively represented by the majority of alumni occupying strategic positions in their careers, the easiness of alumni for getting a job, a large number of individuals in surroundings who are working as a data analysts and representative library. The findings also reveals that all the selected indicators are significant so that no indicators need to be excluded. Evaluation of the model shows that the specified model fits the analyzed data. This is indicated by the Comparative Fit Index (CFI), Root Mean Square Error of Approximation (RMSEA), and Standardized Root Mean Square Residual (SRMR) values ​​which reach good fit criteria.
Bagan Kendali Variansi dengan Penambahan Variabel pada Nilai Ekspor dan Berat Ekspor Widyanti Rahayu; Daisy Salsabilah Kusuma
Jurnal Statistika dan Aplikasinya Vol. 7 No. 2 (2023): Jurnal Statistika dan Aplikasinya
Publisher : LPPM Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.07210

Abstract

To create products or services that meet certain quality standards, supervision and control need to be carried out. Quality control monitors changes in the level of variability, or the level of average, or both to observe changes that occur during the production process. The monitoring stage begins with controlling variability, followed by controlling the average level. Walter A. Shewhart (1920) introduced several variability control charts, including the S2 variance control chart, which involves one quality characteristic, Y. Riaz (2008) introduced a regression-type estimator of the variance of Y using additional correlated information X. This control chart is known as the Vr control chart and shows better results than the S2 control chart. Furthermore, Riaz (2009) introduced a Vt control chart, created based on a ratio-type estimator of the variance of Y. The accuracy of the Vt control chart increases as the value increases. The S2, Vr, Vt control chart is used to monitor the variability of export value (Y) using additional information on export weight (X). All three charts indicate that the variability in export values is out of control. Comparison of the results obtained from the Vt, Vr, and S2 control charts is made by examining the power curve of each chart. Specifically, it was observed that the Vt control chart produces more consistent and accurate conclusions.
Front Matter JSA Volume 7 Issue 2, December 2023 JSA, Journal Editor
Jurnal Statistika dan Aplikasinya Vol. 7 No. 2 (2023): Jurnal Statistika dan Aplikasinya
Publisher : LPPM Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.07200

Abstract

Back Matter JSA Volume 7 Issue 2, December 2023 JSA, Journal Editor
Jurnal Statistika dan Aplikasinya Vol. 7 No. 2 (2023): Jurnal Statistika dan Aplikasinya
Publisher : LPPM Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.07299

Abstract

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